Dynamic simulation and influence evaluation system for atmospheric pollution diffusion process

Abstract

The invention relates to the field of atmospheric pollution diffusion simulation, and particularly discloses an atmospheric pollution diffusion process dynamic simulation and influence evaluation system, which comprises: a) a multi-pollutant collaborative simulation module, which is used for simulating the process of nonlinear interaction between various pollutants through chemical reaction and photochemical reaction, and the diffusion condition of the pollutants in the atmosphere; b) a big data analysis environmental impact assessment module which integrates historical monitoring data and social and economic data information by using a big data analysis technology to construct an assessment model; through the multi-pollutant collaborative simulation module, the system can accurately simulate the nonlinear interaction and diffusion conditions of various pollutants in the atmosphere, the high-precision simulation is beneficial to more accurate understanding of the propagation and conversion process of the atmospheric pollutants, and a solid foundation is provided for subsequent evaluation and strategy formulation.

Description

Technical Field

[0001] The invention belongs to the field of air pollution diffusion simulation, in particular to an air pollution diffusion process dynamic simulation and impact assessment system. Background Art

[0002] Atmospheric pollution diffusion refers to the process by which pollutants in the atmosphere are gradually dispersed and diluted under the mixing effect of turbulence. During this process, pollutants may migrate and transform in different areas as wind direction and speed change, as well as atmospheric temperature and stability.

[0003] Traditional air pollution assessment methods often only focus on the impact of pollutants on human health or the ecological environment, while ignoring the comprehensive impact of other aspects. This results in incomplete assessment results and makes it difficult to provide comprehensive decision support for decision makers. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a dynamic simulation and impact assessment system for atmospheric pollution diffusion process, so as to solve the problem that traditional atmospheric pollution assessment methods in the prior art often only focus on the impact of pollutants on one aspect of human health or ecological environment, while ignoring the comprehensive impact of other aspects.

[0005] A dynamic simulation and impact assessment system for atmospheric pollution diffusion process, comprising:

[0006] a) Multi-pollutant collaborative simulation module, which is used to simulate the process of nonlinear interaction between multiple pollutants through chemical reactions and photochemical reactions, as well as the diffusion of these pollutants in the atmosphere;

[0007] b) Big data analysis environmental impact assessment module, which uses big data analysis technology to integrate historical monitoring data and social and economic data information to build an assessment model to comprehensively assess the impact of air pollution on human health, ecological environment, and economic society;

[0008] c) Dynamic pollution source emission control strategy adjustment module, which combines simulation results and real-time monitoring data and uses intelligent algorithms to dynamically adjust pollution source emission control strategies, including automatically adjusting emission limits and taking other emission reduction measures.

[0009] Preferably, the multi-pollutant collaborative simulation module further comprises:

[0010] a1) Pollutant type and initial concentration input unit, used to define the pollutant types involved in the simulation and their initial concentrations in the atmosphere;

[0011] a2) Atmospheric condition parameter input unit, used to input atmospheric condition parameters that affect simulation results, including wind speed, wind direction, temperature, humidity and light intensity;

[0012] a3) a mathematical model construction unit, used to construct a mathematical model for simulating the nonlinear interaction of multiple pollutants, wherein the mathematical model can reflect the coordinated diffusion and transformation process of pollutants in the atmosphere;

[0013] a4) a high performance computing execution unit, used to execute the mathematical model to efficiently simulate the coordinated diffusion and transformation process of multiple pollutants in the atmosphere;

[0014] a5) Simulation result output unit, used to output simulation results, including the concentration distribution of each pollutant, reaction products and their impact range, to provide basic data for subsequent evaluation and strategy adjustment.

[0015] Preferably, the big data analysis environmental impact assessment module further includes:

[0016] b1) Data collection and integration unit, used to collect and integrate historical monitoring data and socio-economic data from multiple channels;

[0017] b2) an assessment model building unit, which builds an assessment model based on big data analysis technology, wherein the assessment model can quantify the specific impact of air pollution on human health, the ecological environment, and the economy and society;

[0018] b3) Evaluation result output unit, which displays the evaluation results in a visual way, including the contribution of each influencing factor and high-risk areas, to provide intuitive and comprehensive decision support for decision makers.

[0019] Preferably, the dynamic pollution source emission control strategy adjustment module further includes:

[0020] c1) A real-time monitoring data receiving unit, used to receive real-time monitoring data, including the concentration, type and diffusion of pollutants;

[0021] c2) an emission load calculation unit, which calculates the current pollutant emission load according to real-time monitoring data and preset environmental capacity standards;

[0022] c3) Intelligent algorithm optimization unit, which uses intelligent optimization algorithms to dynamically adjust the emission limits of pollution sources and formulate other effective emission reduction measures;

[0023] c4) Adjust the strategy output and monitoring unit, output the adjusted emission control strategy, and monitor its implementation effect to verify the effectiveness of the algorithm and the feasibility of the strategy.

[0024] Preferably, the mathematical expression of the multi-pollutant collaborative simulation module is:

[0025]

[0026]

[0027] Where C(x,y,z,t) is the total concentration of pollutants at location (x,y,z) and time t;

[0028] P i (0) is the initial concentration of the i-th pollutant;

[0029] k i is the reaction rate constant of the i-th pollutant, where it is assumed that its relationship with temperature, humidity and light intensity has been simplified to a constant;

[0030] G i (x, y, t) is the Gaussian diffusion function of the i-th pollutant, which is used to describe the diffusion of the pollutant in the atmosphere;

[0031] D i is the diffusion coefficient of the ith pollutant, u x and u y are the wind speed components in the x and y directions respectively;

[0032] n is the number of pollutant types considered.

[0033] Preferably, the mathematical expression of the big data analysis environmental impact assessment module is as follows:

[0034]

[0035] in:

[0036] E is the comprehensive assessment value of the impact of air pollution on human health, ecological environment, and economic and social development;

[0037] C i (x, y, z, t) is the concentration of the i-th pollutant at position (x, y, z) and time t;

[0038] α i and β i is the corresponding weight coefficient, indicating the relative importance of different pollutants and different impact aspects;

[0039] H i (C i ) is a nonlinear function of the impact of the ith pollutant on human health;

[0040] E i (C i ) is the nonlinear function of the impact of the ith pollutant on the ecological environment;

[0041] V is the volume of the space being evaluated;

[0042] γ is the adjustment coefficient of economic and social impact;

[0043] GDP is the gross domestic product in the assessment area;

[0044] S(C) is a complex nonlinear function of the impact of pollutant concentration on the economy and society, taking into account a variety of socioeconomic factors.

[0045] Preferably, the mathematical expression of the dynamic pollution source emission control strategy adjustment module is as follows:

[0046]

[0047] in:

[0048] L adj is the adjusted pollution source emission limit vector;

[0049] L is the emission limit vector of the pollution source to be optimized;

[0050] C i (x, y, z, t; L) is the concentration of the i-th pollutant at the location (x, y, z) and time t under the emission limit L;

[0051] C lim,i is the concentration limit of the ith pollutant;

[0052] V is the volume of the space being evaluated;

[0053] λ is the regularization coefficient, which is used to balance the concentration deviation and the emission limit adjustment;

[0054] ||L-L0|| is the Euclidean distance between the emission limit adjustment and the initial emission limit L0;

[0055] It means that L is optimized to minimize the objective function.

[0056] Preferably, it also includes a pollution source identification and tracking module, which is used to identify major pollution sources and conduct tracking analysis based on real-time monitoring data and simulation results, so as to provide precise guidance for pollution source management and control.

[0057] Preferably, it also includes an early warning and emergency response module, which is used to warn of possible air pollution incidents based on simulation results and real-time monitoring data, and formulate corresponding emergency response measures.

[0058] Preferably, it also includes a data quality control and verification module for performing quality control and verification on the input monitoring data to ensure the accuracy and reliability of the data and improve the accuracy of simulation and evaluation.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] Through the multi-pollutant collaborative simulation module, the system can accurately simulate the nonlinear interactions and diffusion of multiple pollutants in the atmosphere. This high-precision simulation helps to more accurately understand the propagation and transformation process of atmospheric pollutants, providing a solid foundation for subsequent evaluation and strategy formulation;

[0061] The big data analysis environmental impact assessment module uses big data technology to integrate historical monitoring data and social and economic data to build an assessment model that can comprehensively assess the specific impacts of air pollution on human health, the ecological environment, and the economy and society;

[0062] The dynamic pollution source emission control strategy adjustment module combines real-time monitoring data and simulation results, and uses intelligent algorithms to dynamically adjust the pollution source emission control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the system of the present invention;

[0064] Figure 2 It is a schematic diagram of the process of the multi-pollutant collaborative simulation module of the present invention;

[0065] Figure 3 It is a flow chart of the big data analysis environmental impact assessment module of the present invention;

[0066] Figure 4 It is a flow chart of the dynamic pollution source emission control strategy adjustment module of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] like Figures 1 to 4 As shown:

[0069] Embodiment 1: The present invention provides a system for dynamic simulation and impact assessment of atmospheric pollution diffusion process, comprising:

[0070] a) Multi-pollutant collaborative simulation module, which is used to simulate the process of nonlinear interaction between multiple pollutants through chemical reactions and photochemical reactions, as well as the diffusion of these pollutants in the atmosphere;

[0071] b) Big data analysis environmental impact assessment module, which uses big data analysis technology to integrate historical monitoring data and social and economic data information to build an assessment model to comprehensively assess the impact of air pollution on human health, ecological environment, and economic society;

[0072] c) Dynamic pollution source emission control strategy adjustment module, which combines simulation results and real-time monitoring data and uses intelligent algorithms to dynamically adjust pollution source emission control strategies, including automatically adjusting emission limits and taking other emission reduction measures.

[0073] Specifically, the multi-pollutant collaborative simulation module further includes:

[0074] a1) Pollutant type and initial concentration input unit, used to define the pollutant types involved in the simulation and their initial concentrations in the atmosphere;

[0075] a2) Atmospheric condition parameter input unit, used to input atmospheric condition parameters that affect simulation results, including wind speed, wind direction, temperature, humidity and light intensity;

[0076] a3) a mathematical model construction unit, used to construct a mathematical model for simulating the nonlinear interaction of multiple pollutants, wherein the mathematical model can reflect the coordinated diffusion and transformation process of pollutants in the atmosphere;

[0077] a4) a high performance computing execution unit, used to execute the mathematical model to efficiently simulate the coordinated diffusion and transformation process of multiple pollutants in the atmosphere;

[0078] a5) Simulation result output unit, used to output simulation results, including the concentration distribution of each pollutant, reaction products and their impact range, to provide basic data for subsequent evaluation and strategy adjustment.

[0079] Specifically, the big data analysis environmental impact assessment module further includes:

[0080] b1) Data collection and integration unit, used to collect and integrate historical monitoring data and socio-economic data from multiple channels;

[0081] b2) an assessment model building unit, which builds an assessment model based on big data analysis technology, wherein the assessment model can quantify the specific impact of air pollution on human health, the ecological environment, and the economy and society;

[0082] b3) Evaluation result output unit, which displays the evaluation results in a visual way, including the contribution of each influencing factor and high-risk areas, to provide intuitive and comprehensive decision support for decision makers.

[0083] Specifically, the dynamic pollution source emission control strategy adjustment module further includes:

[0084] c1) A real-time monitoring data receiving unit, used to receive real-time monitoring data, including the concentration, type and diffusion of pollutants;

[0085] c2) an emission load calculation unit, which calculates the current pollutant emission load according to real-time monitoring data and preset environmental capacity standards;

[0086] c3) Intelligent algorithm optimization unit, which uses intelligent optimization algorithms to dynamically adjust the emission limits of pollution sources and formulate other effective emission reduction measures;

[0087] c4) Adjust the strategy output and monitoring unit, output the adjusted emission control strategy, and monitor its implementation effect to verify the effectiveness of the algorithm and the feasibility of the strategy.

[0088] From the above, we can see that this system integrates three modules: multi-pollutant collaborative simulation, big data analysis environmental impact assessment, and dynamic pollution source emission control strategy adjustment. Among them, the multi-pollutant collaborative simulation module can accurately simulate the nonlinear interactions and diffusion of multiple pollutants in the atmosphere; the big data analysis environmental impact assessment module uses big data technology to integrate multi-party data, build an assessment model, and comprehensively assess the impact of air pollution on human health, ecological environment, and economic society; the dynamic pollution source emission control strategy adjustment module combines real-time monitoring data and simulation results, and uses intelligent algorithms to dynamically adjust pollution source emission control strategies; through efficient data processing and intelligent algorithms, the entire system realizes accurate simulation of the atmospheric pollution diffusion process and comprehensive assessment of the impact, and can flexibly adjust pollution source emission control strategies according to real-time conditions, providing strong technical support for environmental protection and pollution control, and helping to formulate more scientific and effective pollution prevention and control strategies.

[0089] Embodiment 2: This embodiment is basically the same as the previous embodiment, except that the mathematical expression of the multi-pollutant collaborative simulation module is:

[0090]

[0091]

[0092] Where C(x,y,z,t) is the total concentration of pollutants at location (x,y,z) and time t;

[0093] P i (0) is the initial concentration of the i-th pollutant;

[0094] k i is the reaction rate constant of the i-th pollutant, where it is assumed that its relationship with temperature, humidity and light intensity has been simplified to a constant;

[0095] G i(x, y, t) is the Gaussian diffusion function of the i-th pollutant, which is used to describe the diffusion of the pollutant in the atmosphere;

[0096] D i is the diffusion coefficient of the ith pollutant, u x and u y are the wind speed components in the x and y directions respectively;

[0097] n is the number of pollutant types considered;

[0098] The specific application process of the above formula is as follows:

[0099] 1. Determine the research area and pollutant types

[0100] First, the scope of the study area is clarified, including geographical coordinates, altitude, etc., and the types of pollutants to be simulated are determined. For example, in an industrial park, the simulated pollutants include sulfur dioxide, nitrogen oxides, particulate matter, etc.

[0101] 2. Collect initial data

[0102] Collect the initial concentration data of each pollutant, which can be obtained through field monitoring or by consulting relevant literature. At the same time, collect meteorological data in the study area, including temperature, humidity, wind speed, wind direction, etc., because these factors will affect the diffusion and transformation of pollutants.

[0103] 3. Set parameters

[0104] According to the above formula, the reaction rate constant k of each pollutant is set i , diffusion coefficient D i And the wind speed component u x and u y These parameters can be estimated through experimental determination, literature review or empirical formulas.

[0105] 4. Build the model

[0106] Substituting the collected data and set parameters into the above formula, a multi-pollutant collaborative simulation model is constructed. This model will be able to describe the diffusion and transformation process of pollutants in the atmosphere and calculate the concentration of pollutants at any location and time.

[0107] 5. Run the model

[0108] Use appropriate computing tools (such as computer programs or mathematical modeling software) to run the constructed model. During the operation, different meteorological conditions and pollution source intensities can be input to simulate the diffusion and transformation of pollutants under different scenarios.

[0109] 6. Analyze the results

[0110] After the model runs, it will output results such as pollutant concentration distribution diagrams and time series diagrams. By analyzing these results, we can understand the diffusion range of pollutants, concentration change trends, and the interaction between different pollutants.

[0111] 7. Model optimization and verification

[0112] Based on the analysis results, the model can be optimized to improve the accuracy and reliability of the simulation. At the same time, the model can be verified through field monitoring or other verification methods to ensure that it can truly reflect the actual situation.

[0113] 8. Application and suggestions

[0114] Finally, according to the simulation results and analysis conclusions, corresponding environmental protection measures and suggestions can be put forward. For example, for areas with high pollutant concentrations, measures such as reducing pollution source emissions and strengthening ventilation can be taken to reduce pollutant concentrations; for sensitive populations or important facilities, protective measures or relocation measures can be taken to reduce the impact of pollutants.

[0115] Through the above steps, the multi-pollutant collaborative simulation module can play an important role in practical applications and provide scientific basis and technical support for environmental protection and pollution control.

[0116] Specifically, the mathematical expression of the big data analysis environmental impact assessment module is as follows:

[0117]

[0118] in:

[0119] E is the comprehensive assessment value of the impact of air pollution on human health, ecological environment, and economic and social development;

[0120] C i (x, y, z, t) is the concentration of the i-th pollutant at position (x, y, z) and time t;

[0121] α i and β i is the corresponding weight coefficient, indicating the relative importance of different pollutants and different impact aspects;

[0122] H i (C i ) is a nonlinear function of the impact of the ith pollutant on human health;

[0123] E i (C i ) is the nonlinear function of the impact of the ith pollutant on the ecological environment;

[0124] V is the volume of the space being evaluated;

[0125] γ is the adjustment coefficient of economic and social impact;

[0126] GDP is the gross domestic product in the assessment area;

[0127] S(C) is a complex nonlinear function of the impact of pollutant concentration on the economy and society, taking into account a variety of socioeconomic factors;

[0128] The specific application process of the above formula is as follows:

[0129] 1. Application of Big Data Analysis Environmental Impact Assessment Module

[0130] Data collection and preprocessing

[0131] Collect concentration data of various pollutants in the study area, including historical data and real-time monitoring data.

[0132] Collect data related to human health, ecological environment, and economic and social impacts, such as hospital visit records, ecosystem monitoring data, GDP, etc.

[0133] The collected data are cleaned, organized and normalized to ensure the accuracy and consistency of the data.

[0134] Model construction and parameter setting

[0135] According to the above formula, a big data analysis environmental impact assessment model is constructed.

[0136] Set the weight coefficient α of various pollutants i and β i ,These coefficients can be determined through expert scoring, questionnaire survey or data analysis.

[0137] Set the health impact function H i (C i ) and ecological impact function E i (C i ) in the form and parameters, these functions can be fitted through regression analysis, machine learning and other methods.

[0138] Set the form and parameters of the economic and social impact adjustment coefficient γ and the socioeconomic impact function S(C).

[0139] Model operation and result analysis

[0140] The preprocessed data is input into the model to perform environmental impact assessment calculations.

[0141] Output the comprehensive evaluation value E and analyze the contribution of different pollutants and different impacts.

[0142] Based on the assessment results, major environmental problems and high-risk areas are identified to provide a basis for subsequent pollution control and management.

[0143] Specifically, the mathematical expression of the dynamic pollution source emission control strategy adjustment module is as follows:

[0144]

[0145] in:

[0146] L adj is the adjusted pollution source emission limit vector;

[0147] L is the emission limit vector of the pollution source to be optimized;

[0148] C i (x, y, z, t; L) is the concentration of the i-th pollutant at the location (x, y, z) and time t under the emission limit L;

[0149] C lim,i is the concentration limit of the ith pollutant;

[0150] V is the volume of the space being evaluated;

[0151] λ is the regularization coefficient, which is used to balance the concentration deviation and the emission limit adjustment;

[0152] ||L-L0|| is the Euclidean distance between the emission limit adjustment and the initial emission limit L0;

[0153] It means optimizing L to minimize the objective function;

[0154] The specific application process of the above formula is as follows:

[0155] Current situation assessment and goal setting

[0156] Based on big data analysis, the results of the environmental impact assessment module are used to determine the current environmental status and problems.

[0157] Set pollution control targets, such as reducing the concentration of a certain pollutant to below a certain limit.

[0158] Model construction and parameter setting

[0159] According to the above formula, a dynamic pollution source emission control strategy adjustment model is constructed.

[0160] Set concentration limits for various pollutants C lim,i .

[0161] The regularization coefficient λ is set to balance the relationship between concentration deviation and emission limit adjustment.

[0162] Set an initial emission limit L0, which can be the current emission standard or policy requirement.

[0163] Model optimization and strategy adjustment

[0164] Use optimization algorithms (such as gradient descent, genetic algorithm, etc.) to optimize the model to minimize the objective function.

[0165] Output adjusted emission limit value L adj , which is a new pollution source emission control strategy.

[0166] Analyze the extent to which the adjusted strategy will improve environmental impacts, as well as the possible economic and social costs.

[0167] Strategy implementation and effect monitoring

[0168] The adjusted emission limit value L adj Applied to actual pollution source management and control.

[0169] Monitor changes in pollutant concentrations in real time and evaluate the effectiveness of strategy implementation.

[0170] Based on the monitoring results, the strategy will be further adjusted and optimized to continuously improve environmental quality.

[0171] As can be seen from the above, in this embodiment, the multi-pollutant collaborative simulation module accurately describes the diffusion and transformation process of pollutants in the atmosphere through complex mathematical formulas, taking into account factors such as the initial concentration of pollutants, reaction rate, Gaussian diffusion function, and wind speed; the big data analysis environmental impact assessment module quantifies the impact of air pollution on human health, ecological environment, and economic society by constructing a comprehensive assessment model, and takes into account the weight coefficients and nonlinear influence functions of different pollutants and different impact aspects; the dynamic pollution source emission control strategy adjustment module uses mathematical optimization methods to dynamically adjust the pollution source emission limits according to real-time monitoring data and simulation results, so as to minimize the objective function of pollutant concentration deviation and emission limit adjustment amount; the entire system realizes comprehensive assessment and dynamic control of the diffusion and impact of air pollution through the precise description of mathematical models and the application of optimization algorithms.

[0172] Embodiment 3: This embodiment is basically the same as the previous embodiment, except that it also includes a pollution source identification and tracking module, which is used to identify major pollution sources and conduct tracking analysis based on real-time monitoring data and simulation results, so as to provide precise guidance for pollution source management and control.

[0173] Specifically, it also includes an early warning and emergency response module, which is used to issue early warnings for possible air pollution incidents based on simulation results and real-time monitoring data, and formulate corresponding emergency response measures.

[0174] Specifically, it also includes a data quality control and verification module, which is used to perform quality control and verification on the input monitoring data to ensure the accuracy and reliability of the data and improve the accuracy of simulation and evaluation.

[0175] From the above, it can be seen that in this embodiment, the newly added pollution source identification and tracking module can accurately identify the main pollution sources and conduct tracking analysis based on real-time monitoring data and simulation results, providing a scientific basis for the effective management and control of pollution sources; at the same time, the addition of the early warning and emergency response module enables the system to timely warn of possible air pollution incidents based on simulation and real-time monitoring data, and quickly formulate emergency response measures to reduce the impact of pollution on the environment and human health; in addition, the introduction of the data quality control and verification module ensures the accuracy and reliability of the input monitoring data, thereby improving the accuracy of the simulation and evaluation of the entire system. These newly added functional modules together constitute a more complete and efficient air pollution diffusion and impact assessment system.

[0176] The standard parts used in the present invention can all be purchased from the market, and the special-shaped parts can be customized according to the description and the drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. The machinery, parts and equipment all adopt conventional models in the prior art, and the circuit connection adopts the conventional connection method in the prior art, which will not be described in detail here. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0177] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0178] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0179] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0180] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0181] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0182] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dynamic simulation and impact assessment system for atmospheric pollution diffusion process, characterized in that: include: a) Multi-pollutant collaborative simulation module, which is used to simulate the process of nonlinear interaction between multiple pollutants through chemical reactions and photochemical reactions, as well as the diffusion of these pollutants in the atmosphere; b) Big data analysis environmental impact assessment module, which uses big data analysis technology to integrate historical monitoring data and social and economic data information to build an assessment model to comprehensively assess the impact of air pollution on human health, ecological environment, and economic society; c) Dynamic pollution source emission control strategy adjustment module, which combines simulation results and real-time monitoring data and uses intelligent algorithms to dynamically adjust pollution source emission control strategies, including automatically adjusting emission limits and taking other emission reduction measures.

2. A system for dynamic simulation and impact assessment of air pollution diffusion process as claimed in claim 1, characterized in that: The multi-pollutant collaborative simulation module further includes: a1) Pollutant type and initial concentration input unit, used to define the pollutant types involved in the simulation and their initial concentrations in the atmosphere; a2) Atmospheric condition parameter input unit, used to input atmospheric condition parameters that affect simulation results, including wind speed, wind direction, temperature, humidity and light intensity; a3) a mathematical model construction unit, used to construct a mathematical model for simulating the nonlinear interaction of multiple pollutants, wherein the mathematical model can reflect the coordinated diffusion and transformation process of pollutants in the atmosphere; a4) a high performance computing execution unit, used to execute the mathematical model to efficiently simulate the coordinated diffusion and transformation process of multiple pollutants in the atmosphere; a5) Simulation result output unit, used to output simulation results, including the concentration distribution of each pollutant, reaction products and their impact range, to provide basic data for subsequent evaluation and strategy adjustment.

3. A system for dynamic simulation and impact assessment of air pollution diffusion process as claimed in claim 2, characterized in that: The big data analysis environmental impact assessment module further includes: b1) Data collection and integration unit, used to collect and integrate historical monitoring data and socio-economic data from multiple channels; b2) an assessment model building unit, which builds an assessment model based on big data analysis technology, wherein the assessment model can quantify the specific impact of air pollution on human health, the ecological environment, and the economy and society; b3) Evaluation result output unit, which displays the evaluation results in a visual way, including the contribution of each influencing factor and high-risk areas, to provide intuitive and comprehensive decision support for decision makers.

4. A system for dynamic simulation and impact assessment of air pollution diffusion process as claimed in claim 3, characterized in that: The dynamic pollution source emission control strategy adjustment module further includes: c1) A real-time monitoring data receiving unit, used to receive real-time monitoring data, including the concentration, type and diffusion of pollutants; c2) an emission load calculation unit, which calculates the current pollutant emission load according to real-time monitoring data and preset environmental capacity standards; c3) Intelligent algorithm optimization unit, which uses intelligent optimization algorithms to dynamically adjust the emission limits of pollution sources and formulate other effective emission reduction measures; c4) Adjust the strategy output and monitoring unit, output the adjusted emission control strategy, and monitor its implementation effect to verify the effectiveness of the algorithm and the feasibility of the strategy.

5. A system for dynamic simulation and impact assessment of air pollution diffusion process as claimed in claim 4, characterized in that: Mathematical expression of the multi-pollutant collaborative simulation module: Where C(x,y,z,t) is the total concentration of pollutants at location (x,y,z) and time t; P i (0) is the initial concentration of the i-th pollutant; k i is the reaction rate constant of the i-th pollutant, where it is assumed that its relationship with temperature, humidity and light intensity has been simplified to a constant; G i (x, y, t) is the Gaussian diffusion function of the i-th pollutant, which is used to describe the diffusion of the pollutant in the atmosphere; D i is the diffusion coefficient of the i-th pollutant, u x and u y are the wind speed components in the x and y directions respectively; n is the number of pollutant types considered.

6. A system for dynamic simulation and impact assessment of air pollution diffusion process as claimed in claim 5, characterized in that: The mathematical expression of the big data analysis environmental impact assessment module is as follows: in: E is the comprehensive assessment value of the impact of air pollution on human health, ecological environment, and economic and social development; C i (x, y, z, t) is the concentration of the i-th pollutant at position (x, y, z) and time t; α i and β i is the corresponding weight coefficient, indicating the relative importance of different pollutants and different impact aspects; H i (C i ) is a nonlinear function of the impact of the ith pollutant on human health; E i (C i ) is the nonlinear function of the impact of the i-th pollutant on the ecological environment; V is the volume of the space being evaluated; γ is the adjustment coefficient of economic and social impact; GDP is the gross domestic product in the assessment area; S(C) is a complex nonlinear function of the impact of pollutant concentration on the economy and society, taking into account a variety of socioeconomic factors.

7. A system for dynamic simulation and impact assessment of air pollution diffusion process as claimed in claim 6, characterized in that: The mathematical expression of the dynamic pollution source emission control strategy adjustment module is as follows: in: L adj is the adjusted pollution source emission limit vector; L is the emission limit vector of the pollution source to be optimized; C i (x, y, z, t; L) is the concentration of the i-th pollutant at the location (x, y, z) and time t under the emission limit L; C lim,i is the concentration limit of the ith pollutant; V is the volume of the space being evaluated; λ is the regularization coefficient, which is used to balance the concentration deviation and the emission limit adjustment; ||L-L0|| is the Euclidean distance between the emission limit adjustment and the initial emission limit L0; It means that L is optimized to minimize the objective function.

8. The system for dynamic simulation and impact assessment of air pollution diffusion process according to claim 1, characterized in that: It also includes a pollution source identification and tracking module, which is used to identify major pollution sources and conduct tracking analysis based on real-time monitoring data and simulation results, providing precise guidance for pollution source management and control.

9. The atmospheric pollution diffusion process dynamic simulation and impact assessment system as claimed in claim 1, characterized in that: It also includes an early warning and emergency response module, which is used to warn of possible air pollution incidents based on simulation results and real-time monitoring data, and formulate corresponding emergency response measures.

10. The atmospheric pollution diffusion process dynamic simulation and impact assessment system according to claim 1, characterized in that: It also includes a data quality control and verification module, which is used to perform quality control and verification on the input monitoring data to ensure the accuracy and reliability of the data and improve the accuracy of simulation and evaluation.

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